SynthSeg: Domain Randomisation for Segmentation of Brain MRI Scans of any Contrast and Resolution

Despite advances in data augmentation and transfer learning,
convolutional neural networks (CNNs) have difficulties generalising to
unseen target domains. When applied to segmentation of brain MRI
scans, CNNs are highly sensitive to changes in resolution and
contrast: even within the same MR modality, decreases in performance
can be observed across datasets. We introduce SynthSeg, the first
segmentation CNN agnostic to brain MRI scans of any contrast and
resolution. SynthSeg is trained with synthetic data sampled from a
generative model inspired by Bayesian segmentation. Crucially, we
adopt a domain randomisation strategy where we fully randomise the
generation parameters to maximise the variability of the training
data. Consequently, SynthSeg can segment preprocessed and
unpreprocessed real scans of any target domain, without retraining or
fine-tuning. Because SynthSeg only requires segmentations to be
trained (no images), it can learn from label maps obtained
automatically from existing datasets of different populations (e.g.,
with atrophy and lesions), thus achieving robustness to a wide range
of morphological variability. We demonstrate SynthSeg on 5,500 scans
of 6 modalities and 10 resolutions, where it exhibits unparalleled
generalisation compared to supervised CNNs, test time adaptation, and
Bayesian segmentation.

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